Domain shift is a major area of concern, as data annotation is a challenging procedure even for the simplest semantic segmentation tasks [1]}. Annotating medical images is also expensive, as annotation can be performed only by physicians, who undergo years of training to obtain domain expertise.
Unsupervised domain adaptation (UDA) is a learning setting aimed at reducing domain gap without data annotation in the target domain. The goal is to adapt a source-trained model for improved generalization in the target domain using solely unannotated data [2]}, [3]}, [4]}, [5]}. The core idea in UDA is to achieve knowledge transfer from the source domain to the target domain by aligning the latent features of the two domains in an embedding space. This idea has been implemented either using adversarial learning [6]}, [7]}, [8]}, [9]}, directly minimizing the distance of distributions of the latent features with respect to a probability metric [10]}, [11]}, [12]}, or a combination of the two [13]}, [14]}.
